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. 2016 Feb 18;61(4):520-534.
doi: 10.1016/j.molcel.2016.01.015. Epub 2016 Feb 4.

Allele-Specific Reprogramming of Cancer Metabolism by the Long Non-coding RNA CCAT2

Affiliations

Allele-Specific Reprogramming of Cancer Metabolism by the Long Non-coding RNA CCAT2

Roxana S Redis et al. Mol Cell. .

Erratum in

  • Mol Cell. 2016 Feb 18;61(4):640

Abstract

Altered energy metabolism is a cancer hallmark as malignant cells tailor their metabolic pathways to meet their energy requirements. Glucose and glutamine are the major nutrients that fuel cellular metabolism, and the pathways utilizing these nutrients are often altered in cancer. Here, we show that the long ncRNA CCAT2, located at the 8q24 amplicon on cancer risk-associated rs6983267 SNP, regulates cancer metabolism in vitro and in vivo in an allele-specific manner by binding the Cleavage Factor I (CFIm) complex with distinct affinities for the two subunits (CFIm25 and CFIm68). The CCAT2 interaction with the CFIm complex fine-tunes the alternative splicing of Glutaminase (GLS) by selecting the poly(A) site in intron 14 of the precursor mRNA. These findings uncover a complex, allele-specific regulatory mechanism of cancer metabolism orchestrated by the two alleles of a long ncRNA.

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Figures

Figure 1
Figure 1. CCAT2 regulates cancer metabolism in vitro and in vivo
(A) Glucose uptake, lactate production and oxygen consumption assays in HCT116 stable clones (E – empty control vector, OC1 and OC3 – CCAT2-overexpressing – GG genotype). (B) Glucose uptake, lactate production and oxygen consumption assays in KM12SM cells with CCAT2 downregulation (GT-genotype). (C) Whole cell lysate Glutaminase activity measured in HCT116 CCAT2-overexpressiong G- or T-allele and control cells. (D) Partial least squares discriminant analysis (sPLS-DA) of HCT116 cells stably overexpressing CCAT2 with either G - or T-allele, and control cells (E – empty vector) in vitro allowed an adequate classification of the different cell lines according to its metabolome*. (E) Xenograft tumors derived from the same cell lines were also correctly classified by sPLS-DA analysis. Results are presented as normalized mean values ± SD. See also Fig. S1 and Table S1. *sPLS-DA algorithm allows the classification of the samples based on the different abundances of each metabolite trying to find the maximum covariance between treatments and metabolome, in this way finding the most important metabolites for explaining the different effects of the treatments.
Figure 2
Figure 2. CCAT2 induces the preferential splicing of GAC
(A) Western Blot analysis of GAC, KGA in HCT116 CCAT2-overexpressing (OC1 and OC3) and control cells. (B) RT-qPCR assessing the mRNA expression of GAC and KGA in HCT116 CCAT2-overexpressing cells (OC1 and OC3) and control cells. (C) Western Blot analysis of GAC, KGA in KM12SM cells with CCAT2 downregulation. (D) Western Blot analysis of GAC, KGA in HCT116 stably overexpressing CCAT2 G- or T-allele and control cells. (E) Fluorescence microscopy images of HCT116 stable clones (E – empty control vector, OC1 and OC3 – CCAT2-overexpressing) transfected with the RG6 Intron 14 vector and the analysis of the EGFP/dsRED ratio. (F) Fluorescence microscopy images of HCT116 CCAT2 G-allele and T-allele transfected with the RG6 intron 14 vector and the analysis of the EGFP/dsRED ratio. Results are presented as normalized mean values ± SD. See also Fig. S2 and Table 2.
Figure 3
Figure 3. CFIm protein complex binds GLS pre-mRNA
(A) Western Blot analysis of CFIm25, GAC and KGA in HCT116 OC1 and KM12SM cells transiently transfected with siRNA for NUDT21, GLS (targeting the coding sequence shared by the two isoforms) and siRNA control. (B) RT-qPCR assessing the GAC/KGA mRNA ratio in HCT116 CCAT2-overexpressing cells (OC1 and OC3) and control cells (E) with modulated CFIm25 expression (C) Western Blot analysis of GAC and KGA in HCT116 OC1 cells with transient blockage of CFIm25 binding motifs (UGUA) by antisense oligonucleotides (ASOs). Schematic representation of the mechanism is presented below. (D) RT-qPCR assessing the fold enrichment of GLS, CCAT2, NEAT1 and GAS5 RNA bound to CFIm25 protein (RNA immunoprecipitation). Data are presented as fold enrichment ratios between control HCT116 cells (E) and CCAT2-overexpressing G- or T-allele. RT-qPCR assessing the fold enrichment of GLS mRNA (E) and CCAT2 (F) bound to CFIm25 and CFIm68 in HCT116 cells CCAT2-overexpressing G- or T-allele and control cells (E). (G) Fluorescence microscopy images of KM12SM cells transfected with siNUDT21 and scr, followed by transfection with the RG6 intron 14 vector, and the analysis of the EGFP/dsRED ratio. Results are presented as normalized mean values ± SD. See also Fig. S3 and Table 2.
Figure 4
Figure 4. The rs6983267 SNP afftects the interaction of CCAT2 with the CFIm protein complex
(A) Western Blot analysis of the proteins pulled down with the MS2-CCAT2 vectors (G, T, A, C and DEL) showing the presence of CFIm25 for the G allele and CFIm68 for the T-allele. (B) Northern Blot analysis showing the presence of CCAT2 and intron 14 (600bp fragment) in the lysate pulled down with TALON resin (upper panel). Western Blot analysis showing the presence of the His6-tagged CFIm complex in the lysate pulled down with the TALON resin (lower panel). (C) Northern Blot analysis showing the presence of CCAT2 and intron 14 (600bp fragment) in the lysate pulled down with Streptavidin beads. Lane 1 marked with the star symbol is identical to lane 3 in Fig. S4D. Schematic illustration of the interaction of CCAT2 with GLS pre-mRNA (representation is not at scale) (D) Northern Blot analysis showing the presence of CCAT2 and intron 14 (600bp fragment) in the lysate pulled down with Streptavidin beads (upper panel). Western Blot analysis showing the presence CFIm25, monomer (26 kDa) and dimer (64 kDa), and His6-tagged CFIm68 (38 kDa) (lower panel). (E, F) AFM images of CCAT2:CFIm:intron 14 quaternary complex including either CCAT2 T-allele (E) or CCAT2 G-allele (F). See also Fig. S4, S5 and Table 3.
Figure 5
Figure 5. GLS promotes in vivo metastases and in vitro cell proliferation and migration
(A) Migration of HCT116 OC1 cells (GG genotype) treated with the inhibitor 968 and DMSO (left panel) and with siGLS and scrambled siRNA (right panel). (B) Migration of KM12SM cells (GT genotype) with stable downregulation of GAC. KM12SM shGFP cells represent the control cells. (C) Number of the lung micrometastases for two groups (shGFP – 4 mice and shGAC – 4 mice) assessed by IHC (left panel). IHC images showing micrometastases in the three groups and images showing the presence or absence of lung metastases for mice injected in the tail-vein with KM12SM shGFP and shGAC cells, respectively (right panel). (D, E) Growth curves for HCT116 OC1 and control cells (GG genotype) treated with DMSO (control) or the GLS allosteric inhibitor 968 (10 μM) (D) and KM12SM cells (GT genotype) with stable downregulation of GAC (E). Glutamine and glutamate concentration in the media relative to the empty well 24 hours after seeding HCT116 CCAT2-overexpressing cells (OC1 – GG genotype) transfected with siRNA against NUDT21 and scrambled (F) and ASOs for inhibiting the binding sites of CFIm25 (G). Results are presented as normalized mean values ± SD. See also Fig. S6 and Table 4.
Figure 6
Figure 6. CCAT2, NUDT21 (CFIm25), CPSF6 (CFIm68) and GLS (GAC and KGA) expression pattern in TCGA dataset and CRC patient samples
(A, B, C and D) Analysis of NUDT21, CPSF6, GAC and KGA mRNA expression in TCGA RNA-Seq colon cancer sample set. (E). Association of GAC mRNA expression with the genotypes (GG, GT and TT) of the rs6983267 SNP for CRC patients (TCGA RNA-Seq dataset). (F) Western Blot analysis of CFIm25, GAC and KGA expression in paired CRC samples (Patient cohort #1). (G) RT-qPCR analysis for CCAT2 in the same paired CRC samples (Patient cohort #1). Results are presented as normalized mean values ± SD. See also Fig. S7.
Figure 7
Figure 7. CCAT2 gene signature in colon cancer patients (TCGA dataset)
Genes associated with CCAT2 were analyzed by Gene Set Enrichment Analysis (GSEA) (A) and Ingenuity Pathway Analysis (IPA - Qiagen) (B). Relevant examples for each analysis are presented in panels (A) and (B). (C) Table containing the pathways significantly associated with CCAT2 expression and rs6983267 genotype (FDR q-val<0.25 and Nom p-val<0.05). Pathways that were positively correlated are marked with red and the ones that are negatively correlated are marked with blue. Highlighted in green are the pathways found common between GSEA analysis and the Affymetrix HTA 2.0 pathways analysis. See also Fig. S7 and Table S4.

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